Nearest orthogonal matrix representation for face recognition

Zhang, Jian; Yang, Jian; Qian, Jianjun; Xu, Jiawei

doi:10.1016/j.neucom.2014.09.019

Cited by 15 publications

(7 citation statements)

References 43 publications

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“…It is observed that the percentage recognition rate is high i.e., 95.3% compared to low value of existing algorithms [9], [10], [11] for the ORL database. The percentage recognition rate of FRDF is 90.25% compared to existing algorithm presented in Zhang et al, [12] DTCWT and FFT are fused using arithmetic addition to obtain final features to identify face images accurately. Table 6 gives Comparison of Recognition Rate of FROCF algorithm with other Existing Algorithms [9], [10], [11] for ORL Database.…”

Section: Comparison Of Recognition Rate With Different Algorithmsmentioning

confidence: 94%

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Face Recognition based on Oriented Complex Wavelets and FFT

Rangaswamy¹,

Raja²,

Venugopal³

2015

IJCA

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Section: Comparison Of Recognition Rate With Different Algorithmsmentioning

confidence: 94%

“…Table 7. gives the comparison of Recognition Rate of FROCF algorithm in comparison with Zhang et al, [12]. …”

Section: Comparison Of Recognition Rate With Different Algorithmsmentioning

confidence: 99%

Face Recognition based on Oriented Complex Wavelets and FFT

Rangaswamy¹,

Raja²,

Venugopal³

2015

IJCA

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“…Ear recognition still represents a relatively young research area compared to other biometric modalities. Prob-lems and research questions that are well studied in other fields require more research and provide room for exploration [115], [116], [117]. In this section we briefly describe what we feel are the most important open problems and most promising research directions in the field.…”

Section: Open Questions and Research Directionsmentioning

confidence: 99%

Ear recognition: More than a survey

2017

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Automatic identity recognition from ear images represents an active field of research within the biometric community. The ability to capture ear images from a distance and in a covert manner makes the technology an appealing choice for surveillance and security applications as well as other application domains. Significant contributions have been made in the field over recent years, but open research problems still remain and hinder a wider (commercial) deployment of the technology. This paper presents an overview of the field of automatic ear recognition (from 2D images) and focuses specifically on the most recent, descriptorbased methods proposed in this area. Open challenges are discussed and potential research directions are outlined with the goal of providing the reader with a point of reference for issues worth examining in the future. In addition to a comprehensive review on ear recognition technology, the paper also introduces a new, fully unconstrained dataset of ear images gathered from the web and a toolbox implementing several stateof-the-art techniques for ear recognition. The dataset and toolbox are meant to address some of the open issues in the field and are made publicly available to the research community. Index Terms-biometry, dataset, in-the-wild, unconstrained image, descriptor-based method, open-source toolbox, ear recognition• Survey: We present a comprehensive survey on ear recognition, which is meant to provide researchers in this field with a recent and up-to-date overview of the state-of-technology. We introduce a taxonomy of the existing 2D ear recognition approaches, discuss the characteristics of the technology and review the existing state-of-the-art. Most importantly, we elaborate on the open problems and challenges faced by the technology. • Dataset: We make a new dataset of ear images available to the research community. The dataset, named Annotated Web Ears (AWE), contains images collected from the web and is to the best of our knowledge the first dataset for ear recognition gathered "in the wild". The images of the AWE dataset contain a

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“…The resultant is applied to individual classifier and results from all classifier are finally fused together. Zhang et al [18] presented face recognition method using nearest orthogonal matrix representation (NOMR). Subspace for each image is calculated and uniquely using addition of basis matrices calculated using singular value decomposition (SVD) and classified using nearest neighbor criterion.…”

Section: Literature Reviewmentioning

confidence: 99%

Face recognition using generalized self organising map based on PCNN features

Sharma

Arya

Yadav

2015

2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)

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This paper presents an efficient face recognition method invariant to slight variation in pose, illumination and background where pulse coupled neural network (PCNN) is used to compute time signature used for feature extraction and generalized self organizing map is being used for classification. The efficiency has been significant improvement by combining PCNN which better resemble human brain neurons and possess better computation power in terms of performance than other neural networks. It is coupled with generalized self organizing map for recognition. The proposed method is robust as it provided better result for slight variation of background and pose and recognition time is greatly reduced.

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Nearest orthogonal matrix representation for face recognition

Cited by 15 publications

References 43 publications

Face Recognition based on Oriented Complex Wavelets and FFT

Face Recognition based on Oriented Complex Wavelets and FFT

Ear recognition: More than a survey

Face recognition using generalized self organising map based on PCNN features

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